Fully Automated Detection of Corticospinal Tract Damage in Chronic Stroke Patients

Yang, Ming; Yang, Yaru; Li, Huijun; Lu, Xuesong; Shi, Yongmei; Liu, Bin; Chen, Hua-Jun; Teng, Gao Jun

doi:10.1155/2014/370849

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…DTI processing was performed using FSL, which has been reported in our previous study[ 29 ]. The DTI data were corrected for head movement and eddy current distortions with the non-diffusion-weighted image (the b0 image) as a reference image.…”

Section: Methodsmentioning

confidence: 99%

Combining Diffusion Tensor Imaging and Gray Matter Volumetry to Investigate Motor Functioning in Chronic Stroke

Yang

et al. 2015

PLoS ONE

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Motor impairment after stroke is related to the integrity of the corticospinal tract (CST). However, considerable variability in motor impairment remains unexplained. To increase the accuracy in evaluating long-term motor function after ischemic stroke, we tested the hypothesis that combining diffusion tensor imaging (DTI) and gray matter (GM) volumetry can better characterize long-term motor deficit than either method alone in patients with chronic stroke. We recruited 31 patients whose Medical Research Council strength grade was ≤ 3/5 in the extensor muscles of the affected upper extremity in the acute phase. We used the Upper Extremity Fugl-Meyer (UE-FM) assessment to evaluate motor impairment, and as the primary outcome variable. We computed the fractional anisotropy ratio of the entire CST (CSTratio) and the volume of interest ratio (VOIratio), between ipsilesional and contralesional hemispheres, to explain long-term motor impairment. The results showed that CSTratio, VOIratio of motor-related brain regions, and VOIratio in the temporal lobe were correlated with UE-FM. A multiple regression model including CSTratio and VOIratio of the caudate nucleus explained 40.7% of the variability in UE-FM. The adjusted R2 of the regression model with CSTratio as an independent variable was 29.4%, and that of using VOIratio of the caudate nucleus as an independent variable was 23.1%. These results suggest that combining DTI and GM volumetry may achieve better explanation of long-term motor deficit in stroke patients, than using either measure individually. This finding may provide guidance in determining optimal neurorehabilitative interventions.

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Section: Methodsmentioning

confidence: 99%

Combining Diffusion Tensor Imaging and Gray Matter Volumetry to Investigate Motor Functioning in Chronic Stroke

Yang

et al. 2015

PLoS ONE

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IGM‐based perceptual multimodal medical image fusion using free energy motivated adaptive PCNN

Tang

Tian

2017

Int J Imaging Syst Tech

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Multimodal medical image fusion merges two medical images to produce a visual enhanced fused image, to provide more accurate comprehensive pathological information to doctors for better diagnosis and treatment. In this article, we present a perceptual multimodal medical image fusion method with free energy (FE) motivated adaptive pulse coupled neural network (PCNN) by employing Internal Generative Mechanism (IGM). First, source images are divided into predicted layers and detail layers with Bayesian prediction model. Then to retain human visual system inspired features, FE is used to motivate the PCNN for processing detail layers, and large firing times are selected as coefficients. The predicted layers are fused with the averaging strategy as activity level measurement. Finally, the fused image is reconstructed by merging coefficients in both fused layers. Experimental results visually and quantitatively show that the proposed fusion strategy is superior to the state‐of‐the‐art methods.

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Multistage multimodal medical image fusion model using feature‐adaptive pulse coupled neural network

Singh¹,

Gupta

2020

Int J Imaging Syst Tech

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Medical image fusion focuses to fuse complementary diagnostic details for better visualization of comprehensive information and interpretation of various diseases and its treatment planning. In this paper, a multistage multimodal fusion model is presented based on nonsubsampled shearlet transform (NSST), stationary wavelet transform (SWT), and feature‐adaptive pulse coupled neural network. Firstly, NSST is employed to decompose the source images into optimally sparse multi‐resolution components followed by SWT. Secondly, structural features are extracted by a weighted sum‐modified Laplacian and applied to an adaptive model to map feature weights for low‐band SWT component fusion, and texture feature‐based fusion rule is applied to fuse high‐band SWT components. High‐frequency NSST components are fused using the absolute maximum and sum of absolute difference based rule to retain complex directional details. Experimental results show that the proposed method obtains significantly better fused medical images compared to others with excellent visual quality and improved computational measures.

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Fully Automated Detection of Corticospinal Tract Damage in Chronic Stroke Patients

Cited by 4 publications

References 18 publications

Combining Diffusion Tensor Imaging and Gray Matter Volumetry to Investigate Motor Functioning in Chronic Stroke

Combining Diffusion Tensor Imaging and Gray Matter Volumetry to Investigate Motor Functioning in Chronic Stroke

IGM‐based perceptual multimodal medical image fusion using free energy motivated adaptive PCNN

Multistage multimodal medical image fusion model using feature‐adaptive pulse coupled neural network

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